"An algorithm for identifying nonsystematic delay-discounting data": Correction to Johnson and Bickel (2008).

"An algorithm for identifying nonsystematic delay-discounting data": Correction to Johnson and Bickel (2008). Research Abstract Details 

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"An algorithm for identifying nonsystematic delay-discounting data": Correction to Johnson and Bickel (2008). Abstract Text:

Reports an error in "An algorithm for identifying nonsystematic delay-discounting data" by Matthew W. Johnson and Warren K. Bickel (Experimental and Clinical Psychopharmacology, 2008[Jun], Vol 16[3], 264-274). Several misprints occurred and should read as follows: 1) Warren K. Bickel is with the University of Arkansas for Medical Sciences. It appears correctly in this erratum. 2) The first sentence in the abstract should read "Several discounting studies have used the R(2) measure to identify data with poor fits to a mathematical discounting model as nonsystematic data to be eliminated." 3) In Table 2, the last row of column one should read "Madden opioid". 4) In the last line of the caption of Figure 2, "though" should read "through". (The following abstract of the original article appeared in record 2008-06716-009.) Several previous discounting studies have use the R(2) measure to identify data sets with poor fits to a mathematical discounting model as nonsystematic data to be eliminated before further analyses are conducted. Data from three previous delay-discounting studies (six separate groups, with a total of 161 individuals) were used to demonstrate why using R(2) to assess the fits of discounting data is problematic. A significant, positive correlation between discounting rate parameter and R(2) was found in most groups, showing that R(2) is more stringent as a measure of fit for low discounting rates than for high discounting rates. Furthermore, it is suggested that identifying nonsystematic data based on any measure of fit to a mathematical discounting model may be problematic because it confounds discounting rate comparison with the issue of discounting model assessment. Therefore, a model-free method to identify nonsystematic data is needed. An algorithm for identifying nonsystematic data is presented that is based on the expectation of a monotonically decreasing discounting function. This algorithm identified 13 cases out of the 161 reanalyzed data sets as nonsystematic. These nonsystematic data are presented, along with examples of data not identified as nonsystematic. This algorithm, or modifications of it, may be useful in a variety of human and nonhuman animal discounting studies (e.g., delay discounting, probability discounting) as an alternative to the R(2) measure for identifying nonsystematic data. The algorithm may be used in empirical investigations to improve discounting methodology, and may be used to identify outliers to be removed from analyses. (PsycINFO Database Record (c) 2008 APA, all rights reserved).

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"An algorithm for identifying nonsystematic delay-discounting data": Correction to Johnson and Bickel (2008). Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Experimental and clinical psychopharmacology

VOLUME: 16

Page Numbers: 321

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ISSN: 1064-1297

DAY: 27

MONTH: Aug

YEAR: 2008

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LANGUAGE: eng

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AFFILIATION: Behavioral Pharmacology Research Unit, Johns Hopkins University School of Medicine.

Country: United States

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MEDLINETA: Exp Clin Psychopharmacol

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